The quantification of the stiffness of tissues and organs is useful in the clinical setting to track the progression of disease and to monitor the response of diseased tissues to treatments (e.g., drug regimens, diet/lifestyle changes, chemotherapy, and radiotherapy). For example, the liver experiences the deposition of fibrous tissue in response to infections, alcohol, and malignancy. The degree of fibrosis is currently characterized using needle core biopsies and monitoring indirect clinical markers of liver function (e.g., liver transaminases and coagulation factors). Biopsy, however, is severely limited in that it samples an extremely small value of the liver, while fibrosis occurs over the entire organ. Biopsies are also inherently risky, with the chance of bleeding and puncture of critical adjacent organs. Biopsies, therefore, are not performed very frequently, even though the clinician would like to have the ability to more frequently (and accurately) monitor fibrosis in the liver. Better monitoring may assist clinicians in making decisions to initiate certain therapeutic protocols, to perform organ transplants at a given time, and/or to monitor response to treatments. In addition to assessing liver fibrosis, tissue stiffness measurements could also have clinical utility in various diseases and conditions, including but not limited to steatotic (fatty) liver disease, myopathies associated with decreased muscle tone, and to monitor tumor response to chemotherapeutic and/or radiation treatment. ARFI imaging, as described in U.S. Pat. Nos. 6,371,912 and 6,951,544, generate images of relative tissue stiffness by displaying displacement magnitudes in adjacent structures and tissues.
U.S. Pat. No. 7,252,004 discusses using a focused ultrasound compression wave to cause a shear wave in a medium. Unfocused ultrasound compression waves are then emitted at a fast rate to obtain a succession of images in the medium. The images are processed in deferred time in order to determine the movements of the medium during the propagation of the shear wave. However, these processing techniques can be complicated and time intensive.
U.S. Pat. No. 5,606,971 discusses the potential for using shear waves to characterize the stiffness of materials, but it is unclear as to how to perform actual reconstructions from the shear wave data. The shear wave tracking modalities (such as SSI) typically rely on the inversion of the Helmholtz (transverse wave) equation to estimate shear wave velocity. See J. Bercoff, M. Tanter, and M. Fink. “Supersonic shear imaging: A new technique for soft tissue elasticity mapping.” IEEE Trans. Ultrason., Ferroelec., Freq. Contr., 51(4): 396-409, 2004. and R. Lerner, S. Huang, and K. Parker. “Sonoelasticity images derived from ultrasound signals in mechanically vibrated tissues.” Ultrasound Med. Biol., 16: 231-239, 1990. However, Helmholtz analysis based techniques typically require taking second order displacement derivatives in both space and time. These mathematical operations may amplify noise in the data and make accurate shear wave estimates difficult. Jitter associated with ultrasonically tracking these displacement fields typically requires that significant filtering operations be performed on the displacement data. These filtering operations are computationally intensive and may be difficult to use in real-time processing.